The visual quality of the video is improved by realizing higher resolution and higher frame rate. In order to realize higher frame rate, we propose new frame rate up-conversion method using spatio-temporal convolutional neural network. In recent years, with the development of machine learning techniques such as convolutional neural networks, clearer interpolation frame estimation has been realized. However, with the conventional convolutional neural network method, it is difficult to estimate an accurate interpolation frames for video including complex motion. In order to deal with this problem, we adopted spatio-temporal convolution rather than conventional spatial convolution. Spatio-temporal convolution is thought to be effective for nonlinear motion because it can capture the time change of the motion of the object. We verified the effectiveness of the proposed method by using video data including complex motions such as rotational motion and scaling.
{"title":"Spatio-Temporal Convolutional Neural Network for Frame Rate Up-Conversion","authors":"Yusuke Tanaka, T. Omori","doi":"10.1145/3325773.3325777","DOIUrl":"https://doi.org/10.1145/3325773.3325777","url":null,"abstract":"The visual quality of the video is improved by realizing higher resolution and higher frame rate. In order to realize higher frame rate, we propose new frame rate up-conversion method using spatio-temporal convolutional neural network. In recent years, with the development of machine learning techniques such as convolutional neural networks, clearer interpolation frame estimation has been realized. However, with the conventional convolutional neural network method, it is difficult to estimate an accurate interpolation frames for video including complex motion. In order to deal with this problem, we adopted spatio-temporal convolution rather than conventional spatial convolution. Spatio-temporal convolution is thought to be effective for nonlinear motion because it can capture the time change of the motion of the object. We verified the effectiveness of the proposed method by using video data including complex motions such as rotational motion and scaling.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128470021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bundit Manaskasemsak, Chayada Chanmakho, Jakapong Klainongsuang, A. Rungsawang
Online reviews, an important source of user opinions, help not only other customers to make a decision but also manufacturers to improve quality of their products or services. Due to commercial reasons, untruthful reviews (spam) written to promote or demote certain products rather than they deserve have become a crucial problem. Although existing supervised approaches have shown the effectiveness of spam detection by using statistical learning, they require much expensive cost for labeling the training data. In this paper, we present BeGP that is a graph-partitioned approach for opinion spam detection. A set of characteristic features is first extracted and a user behavioral graph is constructed by connecting reviewers sharing those features to capture their similar behavior. BeGP is a semi-supervised scheme without requiring any training. Hence, it starts with a small subgraph of labeled spammers and afterwards iteratively expands by conducting connected other users as a resulted set of suspects. We demonstrate the effectiveness of BeGP on two real-world review datasets from Yelp.com. The result shows that it outperforms several state-of-the-art methods with accurately identifying spammers as well as review spams within the k-first order of ranking.
{"title":"Opinion Spam Detection through User Behavioral Graph Partitioning Approach","authors":"Bundit Manaskasemsak, Chayada Chanmakho, Jakapong Klainongsuang, A. Rungsawang","doi":"10.1145/3325773.3325783","DOIUrl":"https://doi.org/10.1145/3325773.3325783","url":null,"abstract":"Online reviews, an important source of user opinions, help not only other customers to make a decision but also manufacturers to improve quality of their products or services. Due to commercial reasons, untruthful reviews (spam) written to promote or demote certain products rather than they deserve have become a crucial problem. Although existing supervised approaches have shown the effectiveness of spam detection by using statistical learning, they require much expensive cost for labeling the training data. In this paper, we present BeGP that is a graph-partitioned approach for opinion spam detection. A set of characteristic features is first extracted and a user behavioral graph is constructed by connecting reviewers sharing those features to capture their similar behavior. BeGP is a semi-supervised scheme without requiring any training. Hence, it starts with a small subgraph of labeled spammers and afterwards iteratively expands by conducting connected other users as a resulted set of suspects. We demonstrate the effectiveness of BeGP on two real-world review datasets from Yelp.com. The result shows that it outperforms several state-of-the-art methods with accurately identifying spammers as well as review spams within the k-first order of ranking.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134638019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work proposes a parallel multi-objective evolutionary algorithm based on decomposition for solving constrained multi-objective optimization problems. A representative decomposition-based algorithm, MOEA/D, decomposes multi-objective problems into a number of single-objective sub-problem using weight vectors and a scalarizing function. It keeps only the best solution for each sub-problem and neighbor solutions are used to generate offspring. Therefore, to independently execute solution generation in parallel by using multi-core, at least two solutions have to be included in a core. Hence, maximum parallel number of MOEA/D-based parallel algorithm is the population size over 2. However, in proposed parallel algorithm, it can be the population size since it keeps not only the best feasible solution but also an archive population of useful infeasible solutions for each sub-problem. The experimental results using discrete knapsack problems with 2 objectives and {2, 6, 10} constraints show that the proposed parallel algorithm achieves higher search performance by utilizing infeasible solutions even if the number of parallelization is higher than a parallel decomposition-based algorithm.
{"title":"A Study for Parallelization of Multi-Objective Evolutionary Algorithm Based on Decomposition and Directed Mating","authors":"Minami Miyakawa, Hiroyuki Sato, Yuji Sato","doi":"10.1145/3325773.3325790","DOIUrl":"https://doi.org/10.1145/3325773.3325790","url":null,"abstract":"This work proposes a parallel multi-objective evolutionary algorithm based on decomposition for solving constrained multi-objective optimization problems. A representative decomposition-based algorithm, MOEA/D, decomposes multi-objective problems into a number of single-objective sub-problem using weight vectors and a scalarizing function. It keeps only the best solution for each sub-problem and neighbor solutions are used to generate offspring. Therefore, to independently execute solution generation in parallel by using multi-core, at least two solutions have to be included in a core. Hence, maximum parallel number of MOEA/D-based parallel algorithm is the population size over 2. However, in proposed parallel algorithm, it can be the population size since it keeps not only the best feasible solution but also an archive population of useful infeasible solutions for each sub-problem. The experimental results using discrete knapsack problems with 2 objectives and {2, 6, 10} constraints show that the proposed parallel algorithm achieves higher search performance by utilizing infeasible solutions even if the number of parallelization is higher than a parallel decomposition-based algorithm.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124474532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francois H. Du Plessis, M. D. du Plessis, T. Gibbon
The ability of an Ant Colony Optimization (ACO) algorithm to adapt on a dynamical network is considered. A previous ACO implementation which was tested on a static Optical Burst Switched (OBS) network with impairments has been altered to be simulated on a dynamic network where network links are brought online or offline. The factors affecting the adaptability of an ACO algorithm is studied and a solution to mitigate some of these factors is proposed. This paper shows that the chosen Pheromone Function is the greatest factor affecting an ACO's adaptability during a change and that other factors such as topology and magnitude of change has little to no affect on its adaptability. In an attempt to improve the ACO's adaptability during a change in its network, a sliding window Pheromone Function is proposed and tested yielding positive results.
{"title":"Analysis of Ant Colony Optimization on a Dynamically Changing Optical Burst Switched Network with Impairments","authors":"Francois H. Du Plessis, M. D. du Plessis, T. Gibbon","doi":"10.1145/3325773.3325775","DOIUrl":"https://doi.org/10.1145/3325773.3325775","url":null,"abstract":"The ability of an Ant Colony Optimization (ACO) algorithm to adapt on a dynamical network is considered. A previous ACO implementation which was tested on a static Optical Burst Switched (OBS) network with impairments has been altered to be simulated on a dynamic network where network links are brought online or offline. The factors affecting the adaptability of an ACO algorithm is studied and a solution to mitigate some of these factors is proposed. This paper shows that the chosen Pheromone Function is the greatest factor affecting an ACO's adaptability during a change and that other factors such as topology and magnitude of change has little to no affect on its adaptability. In an attempt to improve the ACO's adaptability during a change in its network, a sliding window Pheromone Function is proposed and tested yielding positive results.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121924887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we proposed a Recurrent Neural Network (RNN) for the classification of epileptic EEG signal. The EEG dataset is first preprocessed using Discrete Wavelet Transform (DWT) to remove noise and extract features. 20 eigenvalues features were extracted and used to train and test our model. Several experiments were conducted to obtain the optimal parameters for the model. Our model was then compared against Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) and Decision Tree (DT). From experimental results, the best generalization of 99% accuracy is obtained with RMSprop at 0.20 dropout and 4 hidden layers for our model. DT classifier performed second best with accuracy of 98% while RF performed the worst at 75% accuracy.
{"title":"Epilepsy Detection in EEG Signal using Recurrent Neural Network","authors":"I. Aliyu, Y. B. Lim, C. Lim","doi":"10.1145/3325773.3325785","DOIUrl":"https://doi.org/10.1145/3325773.3325785","url":null,"abstract":"In this paper, we proposed a Recurrent Neural Network (RNN) for the classification of epileptic EEG signal. The EEG dataset is first preprocessed using Discrete Wavelet Transform (DWT) to remove noise and extract features. 20 eigenvalues features were extracted and used to train and test our model. Several experiments were conducted to obtain the optimal parameters for the model. Our model was then compared against Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF) and Decision Tree (DT). From experimental results, the best generalization of 99% accuracy is obtained with RMSprop at 0.20 dropout and 4 hidden layers for our model. DT classifier performed second best with accuracy of 98% while RF performed the worst at 75% accuracy.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121135241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of Internet technology, users pay more and more attention to the privacy of personal location data. In order to cover up the user's original check-in data information and prevent attackers from using the user's friend relationship to infer the privacy information of a single user, our paper proposed a hybrid privacy protection method based on differential privacy and random perturbation, and combined the user's friend relationship to realize the location recommendation with privacy protection. Data analysis shows that the privacy level can be set by adding different degrees of random noise to achieve the purpose of personalized privacy protection. Furthermore, differential privacy is used to protect the user's friend relationship, which makes the privacy protection effect of the location recommendation method better. Experiments on real datasets, show that this method can protect users' privacy information and at the same time have a certain accuracy of location recommendation.
{"title":"Location Recommendation with Privacy Protection","authors":"Chang Su, Yumeng Chen, Xianzhong Xie","doi":"10.1145/3325773.3325787","DOIUrl":"https://doi.org/10.1145/3325773.3325787","url":null,"abstract":"With the development of Internet technology, users pay more and more attention to the privacy of personal location data. In order to cover up the user's original check-in data information and prevent attackers from using the user's friend relationship to infer the privacy information of a single user, our paper proposed a hybrid privacy protection method based on differential privacy and random perturbation, and combined the user's friend relationship to realize the location recommendation with privacy protection. Data analysis shows that the privacy level can be set by adding different degrees of random noise to achieve the purpose of personalized privacy protection. Furthermore, differential privacy is used to protect the user's friend relationship, which makes the privacy protection effect of the location recommendation method better. Experiments on real datasets, show that this method can protect users' privacy information and at the same time have a certain accuracy of location recommendation.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131842646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Iyad Abu Doush, Mohammad Qasem Bataineh, Mohammed El-Abd
In evolutionary multi-objective optimization, an evolutionary algorithm is used to solve an optimization problem having multiple, and usually conflicting objective functions. Previous proposed approaches to solve multi-objective optimization problems include NSGA-II, MOEA/D, MOPSO, and MOHS/D algorithms. In our previous work, we enhanced the performance of MOHS/D using a hybrid framework with population diversity monitoring. The population diversity was measured every a predetermined number of iterations to either invoke local search or a diversity enhancement mechanism. In this work, two different stopping criteria are compared using four the HS hybrid frameworks we previously proposed. The stopping criteria compared are the moving average and MGBM. The experimental study is carried using the ZDT, DTLZ and CEC2009 benchmarks. The experimental results show that the moving average stopping criteria gives better results for the majority of the datasets.
{"title":"On Different Stopping Criteria for Multi-objective Harmony Search Algorithms","authors":"Iyad Abu Doush, Mohammad Qasem Bataineh, Mohammed El-Abd","doi":"10.1145/3325773.3325774","DOIUrl":"https://doi.org/10.1145/3325773.3325774","url":null,"abstract":"In evolutionary multi-objective optimization, an evolutionary algorithm is used to solve an optimization problem having multiple, and usually conflicting objective functions. Previous proposed approaches to solve multi-objective optimization problems include NSGA-II, MOEA/D, MOPSO, and MOHS/D algorithms. In our previous work, we enhanced the performance of MOHS/D using a hybrid framework with population diversity monitoring. The population diversity was measured every a predetermined number of iterations to either invoke local search or a diversity enhancement mechanism. In this work, two different stopping criteria are compared using four the HS hybrid frameworks we previously proposed. The stopping criteria compared are the moving average and MGBM. The experimental study is carried using the ZDT, DTLZ and CEC2009 benchmarks. The experimental results show that the moving average stopping criteria gives better results for the majority of the datasets.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131934309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fuzzy k-modes (FKM) are variants of fuzzy c-means used for categorical data. The FKM algorithms generally treat feature components with equal importance. However, in clustering process, different feature weights need to be assigned for feature components because some irrelevant features may degrade the performance of the FKM algorithms. In this paper, we propose a novel algorithm, called feature-weighted fuzzy k-modes (FW-FKM), to improve FKM with a feature-weight entropy term such that it can automatically compute different feature weights for categorical data. Some numerical and real data sets are used to compare FW-FKM with some existing methods in the literature. Experimental results and comparisons actually demonstrate these good aspects of the proposed FW-FKM with its effectiveness and usefulness in practice.
{"title":"Feature-Weighted Fuzzy K-Modes Clustering","authors":"Yessica Nataliani, Miin-Shen Yang","doi":"10.1145/3325773.3325780","DOIUrl":"https://doi.org/10.1145/3325773.3325780","url":null,"abstract":"Fuzzy k-modes (FKM) are variants of fuzzy c-means used for categorical data. The FKM algorithms generally treat feature components with equal importance. However, in clustering process, different feature weights need to be assigned for feature components because some irrelevant features may degrade the performance of the FKM algorithms. In this paper, we propose a novel algorithm, called feature-weighted fuzzy k-modes (FW-FKM), to improve FKM with a feature-weight entropy term such that it can automatically compute different feature weights for categorical data. Some numerical and real data sets are used to compare FW-FKM with some existing methods in the literature. Experimental results and comparisons actually demonstrate these good aspects of the proposed FW-FKM with its effectiveness and usefulness in practice.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116909411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Password authentication is the most widely used authentication method in information systems. The traditional proactive password detection method is generally implemented by counting password length, character class number and computing password information entropy to improve password security. However, passwords that pass proactive password detection do not represent that they are secure. In this paper, based on the research of the characteristics of password distribution under big data, we propose an online password guessing method, which collects a dataset of guessing passwords composed of weak passwords, high frequency passwords and personal information related passwords. It is used to guess the 13k password dataset leaked in China's largest ticketing website, China Railways 12306 website. The experimental results show that even if our guess object has passed the strict proactive password detection, we can construct a guessing password dataset contain only 100 passwords, and effectively guess 4.84% of the passwords.
{"title":"An Online Password Guessing Method Based on Big Data","authors":"Zhiyong Li, Tao Li, Fangdong Zhu","doi":"10.1145/3325773.3325779","DOIUrl":"https://doi.org/10.1145/3325773.3325779","url":null,"abstract":"Password authentication is the most widely used authentication method in information systems. The traditional proactive password detection method is generally implemented by counting password length, character class number and computing password information entropy to improve password security. However, passwords that pass proactive password detection do not represent that they are secure. In this paper, based on the research of the characteristics of password distribution under big data, we propose an online password guessing method, which collects a dataset of guessing passwords composed of weak passwords, high frequency passwords and personal information related passwords. It is used to guess the 13k password dataset leaked in China's largest ticketing website, China Railways 12306 website. The experimental results show that even if our guess object has passed the strict proactive password detection, we can construct a guessing password dataset contain only 100 passwords, and effectively guess 4.84% of the passwords.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114658096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Takuya Iwase, R. Takano, Fumito Uwano, Hiroyuki Sato, K. Takadama
In this paper, we proposed Bat Algorithm extending with Dynamic Niche Radius (DNRBA) which enables solutions to locate multiple local and global optima for solving multimodal optimization problems. This proposed algorithm is designed Bat Algorithm (BA) dealing with the exploration and the exploitation search with Niche Radius which is calculated by the fitness landscape and the number of local and global optima to avoid converging solutions at the same optimum. Although the Niche Radius is an effective niching method for locating solutions at the peaks in the fitness landscape, it is not applicable for uneven multimodal functions and easily fails to keep multiple optima. To overcome this problem, we introduce a dynamic niche sharing scheme which is able to adjust the distance of the niche radius in the search process dynamically for the BA. In the experiment, we employ several multimodal functions and compare DNRBA with the conventional BA to evaluate the performance of DNRBA.
{"title":"The Bat Algorithm with Dynamic Niche Radius for Multimodal Optimization","authors":"Takuya Iwase, R. Takano, Fumito Uwano, Hiroyuki Sato, K. Takadama","doi":"10.1145/3325773.3325776","DOIUrl":"https://doi.org/10.1145/3325773.3325776","url":null,"abstract":"In this paper, we proposed Bat Algorithm extending with Dynamic Niche Radius (DNRBA) which enables solutions to locate multiple local and global optima for solving multimodal optimization problems. This proposed algorithm is designed Bat Algorithm (BA) dealing with the exploration and the exploitation search with Niche Radius which is calculated by the fitness landscape and the number of local and global optima to avoid converging solutions at the same optimum. Although the Niche Radius is an effective niching method for locating solutions at the peaks in the fitness landscape, it is not applicable for uneven multimodal functions and easily fails to keep multiple optima. To overcome this problem, we introduce a dynamic niche sharing scheme which is able to adjust the distance of the niche radius in the search process dynamically for the BA. In the experiment, we employ several multimodal functions and compare DNRBA with the conventional BA to evaluate the performance of DNRBA.","PeriodicalId":419017,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123946230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：